Splice‐switch oligonucleotide‐based combinatorial platform prioritizes synthetic lethal targets CHK1 and BRD4 against MYC‐driven hepatocellular carcinoma

Abstract Deregulation of MYC is among the most frequent oncogenic drivers in hepatocellular carcinoma (HCC). Unfortunately, the clinical success of MYC‐targeted therapies is limited. Synthetic lethality offers an alternative therapeutic strategy by leveraging on vulnerabilities in tumors with MYC deregulation. While several synthetic lethal targets of MYC have been identified in HCC, the need to prioritize targets with the greatest therapeutic potential has been unmet. Here, we demonstrate that by pairing splice‐switch oligonucleotide (SSO) technologies with our phenotypic‐analytical hybrid multidrug interrogation platform, quadratic phenotypic optimization platform (QPOP), we can disrupt the functional expression of these targets in specific combinatorial tests to rapidly determine target–target interactions and rank synthetic lethality targets. Our SSO‐QPOP analyses revealed that simultaneous attenuation of CHK1 and BRD4 function is an effective combination specific in MYC‐deregulated HCC, successfully suppressing HCC progression in vitro. Pharmacological inhibitors of CHK1 and BRD4 further demonstrated its translational value by exhibiting synergistic interactions in patient‐derived xenograft organoid models of HCC harboring high levels of MYC deregulation. Collectively, our work demonstrates the capacity of SSO‐QPOP as a target prioritization tool in the drug development pipeline, as well as the therapeutic potential of CHK1 and BRD4 in MYC‐driven HCC.


| INTRODUCTION
MYC is a general transcription factor that functions as a master regulator of cell cycle. As an oncogene, MYC is frequently deregulated across various cancer types, including breast cancer, liver cancer, colorectal carcinoma, multiple myeloma, and lymphomas, frequently inducing a dependency on the oncogene for disease progression. [1][2][3][4][5][6] These tumors are collectively referred to as MYC-driven/deregulated tumors. However, directly targeting MYC has proven to be a challenge in the clinics owing to its general function as a transcription factor necessary for normal cellular physiology, and the absence of a defined three-dimensional pocket for the design of smallmolecule inhibitors. 7,8 There is therefore an unmet need in developing cancer therapies against MYC. To overcome the undruggable nature of MYC, synthetic lethality offers an ideal treatment strategy, where vulnerabilities in MYC-deregulated cancers are leveraged as therapeutic targets. 9 Two key groups of MYC synthetic lethal targets are regulators of MYC transcription (e.g., BRD4 and CDK9) and MYC stability (e.g., aurora kinases A and B, and polo-like kinase 1), regulating the levels of MYC in tumor cells. 3,10-13 MYC vulnerabilities have also been identified in other pathways such as cell proliferation (e.g., CDK1), evasion of apoptosis (e.g., MCL1 and CHK1), cellular metabolism (e.g., glutaminase-1 and LDHA), and biosynthesis (e.g., IMPDH2 and ribosomal DNA transcription). [14][15][16][17][18][19][20] Attempts at impeding MYC function have also been directed at targeting MAX, the main co-transcription factor necessary for MYCdependent transcription. [21][22][23] Despite the plethora of MYC synthetic lethal targets and development of small-molecule inhibitors against them, targeted synthetic lethality therapy against MYCdriven cancers is still not approved for clinical use. 9 Furthermore, it is unlikely that inhibition of each synthetic lethality target is equal in different cancer types where MYC drives pathogenesis. Hence, there is a need to prioritize the synthetic lethality vulnerabilities which offer the greatest therapeutic outcome against specific MYC-driven cancers.
Notably, combination therapies have been gaining traction in cancer therapeutics against MYC due to inevitable acquired and polyclonal resistance against monotherapies. 9 Tumors often acquire drug resistance through loss of drug-to-target binding site, activating alternative oncogenic pathways and expressing efflux pumps to remove the therapeutics. In addition, monotherapies cannot address the differential sensitivity of multiple clones to anticancer drugs due to the inherent heterogeneity within and between tumors. 24 Thus, identifying pairs of synergistic vulnerabilities which are effective against MYC-driven cancers may result in prolonging therapeutic durability and increasing the response rate.
Computational approaches have facilitated the discovery of novel therapeutic targets against cancers in the era of big data. 9,[25][26][27] Leveraging on such computational platforms can therefore facilitate the process of target prioritization and early drug discovery. Here, we employ the use of the quadratic phenotypic optimization platform (QPOP) to characterize and narrow down effective combinations of targets which mediate the best possible treatment outcomes in MYC-driven hepatocellular carcinoma (HCC) as our disease model of choice. QPOP is an experimental-analytical hybrid platform which utilizes the phenotypic response of biological systems to a set of predefined combination of drugs and dosages as the input data for the establishment of a second-order regression model and corresponding parabolic surfaces. 28 QPOP and other similar approaches are premised on the discovery that responses of biological systems to external perturbations can be modeled accurately and robustly with a second-order polynomial equation, and that the effects of any higher-order components are negligible. [28][29][30][31][32] As a phenotypic-driven and unbiased platform, QPOP streamlines the drug combination identification pipeline without any reference to the mechanisms of action of the chosen drugs nor predetermined drug synergism. An orthogonal array composite design (OACD) is utilized to curate the combinations of drugs and dosages against which the cancer cells are screened. To identify the optimal drug combination within a set of six drugs over a range of three concentrations, samples are screened with 50 combinations using the OACD instead of all 3 6 (729) possible combinations, significantly streamlining the drug combination identification pipeline. The OACD is introduced by merging the two-level fractional factorial design and three-level orthogonal array in a single composite study as described previously. 33 This greatly improves the capacity for factor screening and in-depth analyses. In addition, OACD offers a balanced trade-off between estimation efficiency of the model and run-size economy, offering a viable alternative to other composite designs such as central composite design. 33 Phenotypic responses of samples to the OACD combinations are subsequently used to establish a quadratic regression model from which the phenotypic response of biological system to all possible combination of drugs and doses are determined. Prior studies have also demonstrated that a quadratic regression model outperforms neural networks in modeling the nonlinear nature of cellular responses. 29,34 Given a panel of drugs and dosages, QPOP is therefore able to rapidly identify the globally optimized drug combination parameters which yields the greatest therapeutic outcome of interest. Here, we hypothesize that by using dose-dependent RNA therapeutics such as antisense and splice-switching oligonucleotides (ASOs/SSOs) which disrupt target expression at the transcriptional level in place of small molecule inhibitors, QPOP has the additional capacity to prioritize gene targets with the greatest therapeutic capabilities and guide early drug discovery.
ASOs are emerging RNA therapeutics, which exhibit anticancer properties by binding to their specific oncogene targets and manipulating its expression levels. ASO-based therapies function through two different mechanisms-by inducing RNAse-H-dependent mRNA cleavage and by modulating of pre-mRNA splicing events to regulate gene expression. 35 ASOs which act through the latter mechanism are specifically termed as SSOs. SSOs are chemically modified singlestranded RNAs which bind specifically to the splice sites of target mRNAs, to effect steric hindrance and impede splicing events. 36,37 Consequently, SSO-induced exon skipping results in functional knockdown of the target gene and possibly nonsense-mediated decay of the target. 38 To date, 10 oligonucleotide-based drugs have received approval by the FDA for clinical use, of which half are SSOs. [39][40][41][42][43][44] Recent advances in SSO technologies have also demonstrated the value of SSOs as candidate RNA therapeutics with significant anticancer properties in in vivo models of HCC, melanoma, prostate, and lung cancer. 38,[45][46][47] Additionally, prior applications of SSOs have exhibited dose-dependent splice-switch events and is therefore suitable for use in conjunction with QPOP for target prioritization, where the dosages of the therapeutics are considered. 48 Here, we report the development and the use of SSO-QPOP that exhibits a novel function in the target prioritization stage of the drug development pipeline by leveraging on genetic modulators. We propose that the SSO-QPOP-derived combination of CHK1 and BRD4 is a pair of MYC synthetic lethal targets with the greatest therapeutic potential in the treatment of MYC-driven HCC.  Figure 1a.
To determine the appropriate dosages of the two smallmolecule inhibitors, sorafenib and cabozantinib, we first performed a single drug dose-response assay to determine the half-maximal inhibitory concentrations (IC 50 ) of each drug (Figure 1e). IC 15 and IC 30 concentrations were used in the SSO-QPOP combination treatments if the IC 50 was lower than the highest concentrations of each drug available in the blood when administered in patients (C max ). 51 Alternatively, the 5% and 10% C max will be used in the SSO-QPOP combination treatments to ensure that the drug dosages are aligned with the clinically relevant concentrations (Table S2). Lastly, optimization of the SSO-QPOP conditions demonstrated that the transcripts were sufficiently spliced 24 h

| SSO-QPOP identified CHK1 and BRD4 combinations as promising synthetic lethal targets of MYC
Utilizing the OACD, three HCC cell lines, Bel7402, HCCLM3, and SNU387, were then treated with the 50 unique SSO-QPOP combinations for the analysis (Table S1). 33 (Table S3). Notably, the combination of ssCHK1 and ssBRD4 was among the top-ranked two-modulator combinations in both MYC Hi lines, Bel7402 and HCCLM3, outperforming the standard-of-care drugs, while being absent in top-ranked combinations for MYC Lo SNU387 ( Table 1). The bilinear effect of ssCHK1 and ssBRD4 was also significant in only the MYC Hi lines (Table S3).
Interrogation into the parabolic response surface maps for the com-

| Splice-switch oligonucleotides targeting CHK1 and BRD4 induce MYC degradation and apoptosis in MYC Hi HCC
We proceeded to investigate the effects of the combination in Bel7402 to determine the efficacy of ssCHK1 and ssBRD4 in vitro. We additionally confirmed the unique synergy of AZD7764 and OTX-015 in MYC-deregulated HCC via the bliss independence model.
A synergistic drug interaction is defined as one when the observed viability of the cells in vitro is significantly lower than its expected viability-the product of its singlet viabilities. 60   compared to the monotherapies, indicative of enhanced cell killing in vitro (Figures 6c, S4C)  highlighting the clinical relevance HCC-PDXOs as in vitro models of the disease. [80][81][82]

| QPOP model generation
HCC cell lines were first reverse transfected with the SSOs component of the 50 experimental combinations necessary for sufficient factor screening and in-depth analyses based on the OACD (Table S2). 33 ssNeg negative control SSO was used to ensure that the total SSO concentration was kept at a constant of 200 nM for all combinations.
Twenty-four hours after reverse transfection, the cells were treated with the respective sorafenib and cabozantinib components of the 50 combinations for 48 h before their viability were quantified using the CellTiter 96 ® AQ ueous nonradioactive cell proliferation MTS assay as the phenotypic input for QPOP (Tables S1 and S2).
The viability of the HCC cell lines was fitted into a second-order quadratic series as shown as follows: where y is the desired model output, α is the intercept, x j is the jth drug dose, β j is the coefficient for the single-drug dose of the jth drug, β ij is the coefficient for the interaction between the ith and jth drug, and β jj is the quadratic coefficient for the jth drug. MATLAB was used to perform a stepwise regression analysis with the second-order quadratic function to project the expected cell viabilities for all possible combinations as the output. The parabolic response surface maps were also generated with MATLAB.
Additional methods are described in Supplementary Material and Methods S1.

CONFLICT OF INTEREST
Edward Kai-Hua Chow is a shareholder in KYAN Therapeutics.

DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request. Data pertaining to the generation of the QPOP model is not available as itis proprietary to KYAN Therapeutics. SSO sequences are undergoing patent application.